python - 检测人脸然后自动裁剪图片

Question

我正在尝试寻找一款可以在我的图片中检测人脸、使检测到的人脸居中并裁剪图片的 720 x 720 像素的应用程序。编辑我打算这样做的数百张图片相当耗时且细致。

我尝试过使用 python opencv mentioned here但我认为它已经过时了。我也试过using this但这也给我的系统带来了错误。还尝试使用 GIMP 的人脸检测插件，但它是为 GIMP 2.6 设计的，但我经常使用 2.8。我也尝试过在超高博客上发布的内容，但它已经过时了(因为我使用的是 Ubuntu 的 Precise 衍生产品，而博文是在它仍然是 Hardy 的时候发布的)。也尝试使用 Phatch，但没有人脸检测，所以一些裁剪的图片的脸被剪掉了。

我已经尝试了以上所有方法，并浪费了半天时间试图使上述任何一种方法都能完成我需要做的事情。

你们有什么建议可以实现我拥有的大约 800 张图片的目标。

我的操作系统是 Linux Mint 13 MATE。

注意:我打算再添加 2 个链接，但由于我还没有太多声誉，stackexchange 阻止我再发布两个链接。

Answer 1

我已经设法从各种来源获取一些代码并将它们拼接在一起。它仍在进行中。另外，您有任何示例图片吗？

'''
Sources:
http://pythonpath.wordpress.com/2012/05/08/pil-to-opencv-image/
http://www.lucaamore.com/?p=638
'''

#Python 2.7.2
#Opencv 2.4.2
#PIL 1.1.7

import cv
import Image

def DetectFace(image, faceCascade):
    #modified from: http://www.lucaamore.com/?p=638

    min_size = (20,20)
    image_scale = 1
    haar_scale = 1.1
    min_neighbors = 3
    haar_flags = 0

    # Allocate the temporary images
    smallImage = cv.CreateImage(
            (
                cv.Round(image.width / image_scale),
                cv.Round(image.height / image_scale)
            ), 8 ,1)

    # Scale input image for faster processing
    cv.Resize(image, smallImage, cv.CV_INTER_LINEAR)

    # Equalize the histogram
    cv.EqualizeHist(smallImage, smallImage)

    # Detect the faces
    faces = cv.HaarDetectObjects(
            smallImage, faceCascade, cv.CreateMemStorage(0),
            haar_scale, min_neighbors, haar_flags, min_size
        )

    # If faces are found
    if faces:
        for ((x, y, w, h), n) in faces:
            # the input to cv.HaarDetectObjects was resized, so scale the
            # bounding box of each face and convert it to two CvPoints
            pt1 = (int(x * image_scale), int(y * image_scale))
            pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
            cv.Rectangle(image, pt1, pt2, cv.RGB(255, 0, 0), 5, 8, 0)

    return image

def pil2cvGrey(pil_im):
    #from: http://pythonpath.wordpress.com/2012/05/08/pil-to-opencv-image/
    pil_im = pil_im.convert('L')
    cv_im = cv.CreateImageHeader(pil_im.size, cv.IPL_DEPTH_8U, 1)
    cv.SetData(cv_im, pil_im.tostring(), pil_im.size[0]  )
    return cv_im

def cv2pil(cv_im):
    return Image.fromstring("L", cv.GetSize(cv_im), cv_im.tostring())


pil_im=Image.open('testPics/faces.jpg')
cv_im=pil2cv(pil_im)
#the haarcascade files tells opencv what to look for.
faceCascade = cv.Load('C:/Python27/Lib/site-packages/opencv/haarcascade_frontalface_default.xml')
face=DetectFace(cv_im,faceCascade)
img=cv2pil(face)
img.show()

在 Google 的首页上进行测试(Google 搜索的“面孔”):

---

更新

这段代码应该完全符合您的要求。如果您有任何问题，请告诉我。我试图在代码中包含很多注释:

'''
Sources:
http://opencv.willowgarage.com/documentation/python/cookbook.html
http://www.lucaamore.com/?p=638
'''

#Python 2.7.2
#Opencv 2.4.2
#PIL 1.1.7

import cv #Opencv
import Image #Image from PIL
import glob
import os

def DetectFace(image, faceCascade, returnImage=False):
    # This function takes a grey scale cv image and finds
    # the patterns defined in the haarcascade function
    # modified from: http://www.lucaamore.com/?p=638

    #variables    
    min_size = (20,20)
    haar_scale = 1.1
    min_neighbors = 3
    haar_flags = 0

    # Equalize the histogram
    cv.EqualizeHist(image, image)

    # Detect the faces
    faces = cv.HaarDetectObjects(
            image, faceCascade, cv.CreateMemStorage(0),
            haar_scale, min_neighbors, haar_flags, min_size
        )

    # If faces are found
    if faces and returnImage:
        for ((x, y, w, h), n) in faces:
            # Convert bounding box to two CvPoints
            pt1 = (int(x), int(y))
            pt2 = (int(x + w), int(y + h))
            cv.Rectangle(image, pt1, pt2, cv.RGB(255, 0, 0), 5, 8, 0)

    if returnImage:
        return image
    else:
        return faces

def pil2cvGrey(pil_im):
    # Convert a PIL image to a greyscale cv image
    # from: http://pythonpath.wordpress.com/2012/05/08/pil-to-opencv-image/
    pil_im = pil_im.convert('L')
    cv_im = cv.CreateImageHeader(pil_im.size, cv.IPL_DEPTH_8U, 1)
    cv.SetData(cv_im, pil_im.tostring(), pil_im.size[0]  )
    return cv_im

def cv2pil(cv_im):
    # Convert the cv image to a PIL image
    return Image.fromstring("L", cv.GetSize(cv_im), cv_im.tostring())

def imgCrop(image, cropBox, boxScale=1):
    # Crop a PIL image with the provided box [x(left), y(upper), w(width), h(height)]

    # Calculate scale factors
    xDelta=max(cropBox[2]*(boxScale-1),0)
    yDelta=max(cropBox[3]*(boxScale-1),0)

    # Convert cv box to PIL box [left, upper, right, lower]
    PIL_box=[cropBox[0]-xDelta, cropBox[1]-yDelta, cropBox[0]+cropBox[2]+xDelta, cropBox[1]+cropBox[3]+yDelta]

    return image.crop(PIL_box)

def faceCrop(imagePattern,boxScale=1):
    # Select one of the haarcascade files:
    #   haarcascade_frontalface_alt.xml  <-- Best one?
    #   haarcascade_frontalface_alt2.xml
    #   haarcascade_frontalface_alt_tree.xml
    #   haarcascade_frontalface_default.xml
    #   haarcascade_profileface.xml
    faceCascade = cv.Load('haarcascade_frontalface_alt.xml')

    imgList=glob.glob(imagePattern)
    if len(imgList)<=0:
        print 'No Images Found'
        return

    for img in imgList:
        pil_im=Image.open(img)
        cv_im=pil2cvGrey(pil_im)
        faces=DetectFace(cv_im,faceCascade)
        if faces:
            n=1
            for face in faces:
                croppedImage=imgCrop(pil_im, face[0],boxScale=boxScale)
                fname,ext=os.path.splitext(img)
                croppedImage.save(fname+'_crop'+str(n)+ext)
                n+=1
        else:
            print 'No faces found:', img

def test(imageFilePath):
    pil_im=Image.open(imageFilePath)
    cv_im=pil2cvGrey(pil_im)
    # Select one of the haarcascade files:
    #   haarcascade_frontalface_alt.xml  <-- Best one?
    #   haarcascade_frontalface_alt2.xml
    #   haarcascade_frontalface_alt_tree.xml
    #   haarcascade_frontalface_default.xml
    #   haarcascade_profileface.xml
    faceCascade = cv.Load('haarcascade_frontalface_alt.xml')
    face_im=DetectFace(cv_im,faceCascade, returnImage=True)
    img=cv2pil(face_im)
    img.show()
    img.save('test.png')


# Test the algorithm on an image
#test('testPics/faces.jpg')

# Crop all jpegs in a folder. Note: the code uses glob which follows unix shell rules.
# Use the boxScale to scale the cropping area. 1=opencv box, 2=2x the width and height
faceCrop('testPics/*.jpg',boxScale=1)

使用上图，此代码从 59 个人脸中提取 52 个人脸，生成裁剪文件，例如: